The eigenvalue that justify the higher percentage of variability in the data is "Eigenvalue 2" with a variance of 3,64. So the first principal component (PC1) is represented by the "Eigenvalue 2". The other principal component (PC2) is represented by "Eigenvector 1" with an "Eigenvalue 1" of 0,14. Both eigenvalues added are the 100% of the variance (a plane).The total variance did not change respect to the original data, simply the variance explained by every of the new axis has changed, but added they give the same (3,78).